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计算机系统应用:2020,29(8):192-198
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基于CNN和LSTM联合预测并修正的电量缺失数据预测
(1.中国科学院大学, 北京 100049;2.中国科学院 沈阳计算技术研究所, 沈阳 110168)
Prediction and Correction of Power Loss Data Based on CNN and LSTM
(1.University of Chinese Academy of Sciences, Beijing 100049, China;2.Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China)
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投稿时间:2020-02-08    修订日期:2020-03-03
中文摘要: 数据是电网调度控制系统稳定运行的关键依据, 而因为硬件故障等原因导致数据采集过程中的数据缺失会影响到系统数据的完整性, 从而对电网调度的智能性和高效性产生相应的影响. 因此, 针对缺失数据的准确预测对于智能电网调度系统的建设有着重要的意义. 本文针对解决电网领域电能量采集系统的缺失数据预测问题对已有的基于CNN和LSTM联合预测方法进行改进和优化, 在联合预测模型基础上添加修正模型, 针对不同缺失数据段利用CNN卷积神经网络和电力数据里特有的对侧数据场景建模, 实验结果证明该方法将平均绝对误差值降到0.142, 提高了现有预测模型的准确率, 对电网调度系统的智能性和高效性提供了数据完整性、准确性的保障.
中文关键词: 电量缺失数据预测  CNN  LSTM  对侧数据
Abstract:Data is the key basis for the stable operation of the power grid dispatching control system, in the process of data collection, the lack of data due to hardware failure and other reasons will affect the integrity of the system data, which will have a corresponding impact on the intelligence and efficiency of power grid dispatching. Therefore, the accurate prediction of missing data is of great significance for the construction of smart grid dispatching system. In order to solve the problem of missing data prediction of electric energy collection system in the field of power grid, this study improves and optimizes the existing joint prediction method based on CNN and LSTM, adds a modified model on the basis of the joint prediction model, and uses CNN convolution neural network and the unique opposite side data scene modeling in the electric power data for different missing data segments. The experimental results show that this method reduces the average absolute error value to 0.142, which improves the accuracy of the existing prediction model and accuracy guarantee for the intelligence and efficiency of power grid dispatching system.
文章编号:7580     中图分类号:    文献标志码:
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引用文本:
郭蕴颖,丁云峰.基于CNN和LSTM联合预测并修正的电量缺失数据预测.计算机系统应用,2020,29(8):192-198
GUO Yun-Ying,DING Yun-Feng.Prediction and Correction of Power Loss Data Based on CNN and LSTM.COMPUTER SYSTEMS APPLICATIONS,2020,29(8):192-198

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